Optimization of a sustainable closed loop supply chain network design under uncertainty using multi-objective evolutionary algorithms
Bibliographic record
Abstract
Environmental, social and economic concerns have highlighted the importance of closed loop supply chain (CLSC) network design problem according to sustainable development. In addition, the uncertainty in decision elements adds to the complexity of this problem. Hence, this paper aims to propose a fuzzy multi-objective mixed integer linear programming (FMOMILP) model for a multi-echelon and multi-period CLSC network that minimize cost and environmental effects and maximize social impacts, simultaneously. At first, the model is converted into a multi-objective mixed-integer linear programming (MOMILP) model by the weighted average method. Due to NPhardness of the problem, a non-dominated sorting genetic algorithm-II (NSGA-II) is developed to solve this multi-objective mathematical model. The obtained results are validated with the non-dominated ranking genetic algorithm (NRGA), due to there is no benchmark for this problem. In addition, different numerical instances are presented and analyzed with different measures in order to indicate the efficiency of proposed algorithms. The provided results demonstrate that the proposed NSGA-II algorithm is an adequate tool to solve the multi-objective problem of CLSC network design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".